Principal AI Architect Conversational & Agentic AI

At Staffworxs we dont just connect talent we power transformation. Headquartered in Frisco TX with teams in Bengaluru and Hyderabad we combine global reach with deep expertise. Our Digital & Data Analytics practice drives growth and innovation for some of the worlds top brands who continue to retain us as their trusted partner. If youre ready to make an impact youre in the right place.

Job Details:

Job Title: Principal AI Architect - Conversational & Agentic AI
Location: Atlanta GA
Work Mode: 5 days onsite (Let me know if anybody asks for 3-4 days)
Duration: Long term Contract

Job Description Experience

10-15 years overall 5 in GenAI/Agentic AI

Focus

Design and Build conversational AI based product on AWS

Location

Atlanta GA (USA)

About the role
You will work closely with a client team in Atlanta to design and build a conversational AI product on AWS in a fast-paced environment. You will lead requirement gathering with business and technical stakeholders own the end-to-end architecture make fast technical calls and stay hands-on so the team moves from concept to a production-ready release on an accelerated timeline. You are equally comfortable at a whiteboard with client leadership and in a code review with engineers.
What you will do

• Lead requirement gathering: run discovery workshops with business and technical stakeholders map user journeys and conversation flows capture functional and non-functional requirements and turn them into a prioritised backlog with clear acceptance criteria.
• Define the target architecture for the product early and decisively conversational AI RAG and agents on AWS Bedrock/Bedrock AgentCore.
• Design chatbots and assistants that can serve thousands of concurrent users with predictable latency cost and uptime.
• Architect agent ecosystems using MCP for tool and data access and A2A for agent-to-agent collaboration.
• Drive rapid iterative delivery: get to a working MVP quickly then harden load test and take the product to production readiness.
• Build hands-on alongside the team: agent code prompts retrieval pipelines integrations with data platforms and enterprise systems and infrastructure as code.
• Set standards for LLMOps: evaluation prompt and model versioning observability guardrails and cost governance.
• Own non-functional design: security identity and access PII handling compliance resilience and disaster recovery.
• Work as part of the client team: shape scope and trade-offs with stakeholders run weekly demos and present architecture and progress to client leadership.
• Run design reviews mentor engineers and hand over a documented operable platform (runbooks architecture decisions cost model) at the end of the engagement.

Must have

• Enterprise delivery: architected and delivered multiple production-grade GenAI or conversational AI products end to end including at least one taken from concept to production on a tight timeline.
• Embedded fast-paced delivery: worked as part of consulting or client teams; comfortable with ambiguous scope weekly demos and senior stakeholder exposure.
• Requirements and discovery: led discovery and requirement workshops for AI products; able to translate business goals into use cases conversation flows user stories and measurable success criteria.
• Hands-on builder: writes production Python and infrastructure as code (CDK Terraform or CloudFormation) not a diagram-only architect.
• Conversational AI: production chatbots or virtual assistants with multi-turn context intent handling session memory human handoff and multichannel delivery (web mobile messaging contact centre).
• Chatbot scale: designed systems running at high concurrency in production. Candidates should state peak concurrent users p95 latency and daily conversation volume they handled.
• Scale engineering: response streaming provisioned throughput and quota planning semantic and response caching load testing autoscaling and graceful degradation under model rate limits.
• RAG: retrieval pipeline design chunking embeddings hybrid search reranking metadata filtering vector stores (OpenSearch pgvector Aurora Pinecone or similar) and groundedness evaluation.
• AWS Bedrock (essential): foundation model selection Knowledge Bases Guardrails Agents model evaluation and cost optimisation.
• Bedrock AgentCore (essential): Runtime Memory Gateway Identity and Observability for deploying and operating agents securely at scale.
• MCP: designed MCP servers and clients that expose enterprise APIs and data as governed tools including authentication and authorisation.
• A2A and multi-agent: orchestration patterns (supervisor hierarchical peer-to-peer) using A2A and frameworks such as Strands Agents LangGraph or CrewAI.
• Foundations: strong AWS architecture (serverless containers networking IAM security) distributed systems and API design; Python hands-on.
• Responsible AI: guardrails hallucination control prompt-injection defence auditability and data privacy in regulated environments.
• LLM observability and evaluation: Langfuse Ragas Bedrock evaluations or similar tools to trace monitor and evaluate LLM applications.
• Location: based in or able to relocate to Atlanta; US work authorisation required.

Nice to have

• Voice AI with Amazon Connect Lex or speech models.
• Equivalent platforms on Azure OpenAI Vertex AI or Databricks Mosaic AI.
• Fine-tuning distillation or small-model deployment for cost and latency.
• Product mindset: conversational UX design user feedback loops and A/B testing of prompts or flows.
• AWS Certification on AI/GenAI
• Domain experience in Finance

Staffworxs is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive workplace for all employees regardless of race color religion gender sexual orientation national origin age disability or veteran status.

Required Experience:

Staff IC